One of the most fundamental concepts in time series econometrics is stationarity. A stationary time series is one whose statistical properties—such as mean, variance, and autocorrelation—remain constant over time. This concept may appear technical, but it is central to the validity of econometric inference. Much of modern applied econometrics, from forecasting inflation to modelling asset prices, rests on the assumption of stationarity. When this condition is violated, standard results collapse, leading to spurious regressions, misleading inferences, and flawed policy conclusions.
Read MoreThe relationship between economic growth and unemployment has long been a central concern of macroeconomics, particularly for policymakers seeking to balance objectives of stability and full employment. One of the most enduring contributions to this discussion is Okun’s Law, named after the American economist Arthur M. Okun, who first articulated the relationship in the early 1960s. While not a structural law in the sense of immutable causation, Okun’s Law provides a robust empirical regularity linking changes in output to changes in unemployment. Its simplicity and intuitive appeal have made it a cornerstone of applied macroeconomic analysis, even as its exact parameters vary across time and context.
Read MoreFrom its origins, classical macroeconomic theory has rested heavily on Walrasian foundations—markets clearing through price adjustments and equilibrium emerging across interlinked markets. Post‑Walrasian Macroeconomics, however, represents a response to and departure from that orthodoxy, particularly the Dynamic Stochastic General Equilibrium (DSGE) model that came to dominate macroeconomic discourse at the end of the 20th century. Instead, it seeks to craft a richer, more nuanced understanding of real economies—one that embraces complexity, institutions, evolving expectations, and computational methods beyond the neat, equilibrium‑focused paradigm.
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